Stealth, Raider, Nighthawk and Guerilla are terms you might more readily associate with a war zone than with the more sedate world of equities trading. However, these are the rather aggressive names being given to the latest computer-driven trading products.

The military tone may seem a little far-fetched, but the analogy is apt. These computer programmes, known as trading algorithms, help brokers to home in on the best share prices for institutional investors, and are the weapons that help distinguish one sellside firm from another. The ability of these firms to seek out liquidity is more important than it has ever been, as equities markets continue to suffer from a lack of trading volumes and low volatility.

Stephane Loiseau, managing director, deputy global head of execution services at Societe Generale Corporate and Investment Banking, said: “We continue to be in an arms race: you can’t lower your guard on the technology front. If you continue to invest now, you’re effectively best positioned for when market volumes start to pick up again.”

It could be a long wait. Research by Financial News using data provided by Thomson Reuters shows that trading turnover across the US, Europe and Asia declined during April and May compared with the first quarter, dashing hopes that the second quarter would mark the long-awaited upturn.
The level of trading activity is not the only problem. High frequency traders, a fragmentation of liquidity across different venues and a rapidly evolving market structure are all putting a strain on the ability of brokers to serve the buyside.

A growing awareness

So, while the market waits for better times, the middlemen are deploying resources to capturing what little liquidity is left.

A perpetual process of innovation and refinement has led to an array of trading algorithms. These act as the brain for trading programmes, telling the computer what to do and when, shaping aspects of the order, including its timing, price, and quantity. As firms develop algorithms to capture every nuance of the changing trading landscape, these systems increasingly fall into three broad categories: liquidity-seeking algos, which aggressively place orders based on a range of urgency levels; systematic or rule-based algos, which act in relation to a benchmark; and participation strategies, which can be customised for different market conditions.

Though a firm’s algorithm is the key to its trading strategy, and often a closely guarded secret, several strategies have emerged.

Related

While the climate of reduced volumes puts a premium on liquidity-seeking algorithms, according to David Mechner, chief executive of US trading technology firm Pragma, low market volatility is limiting the effectiveness of these aggressive algos, which are designed to complete orders in the least amount of time, from across a range of venues.

Mechner said: “There has been a shift away from aggressive, liquidity-sourcing algorithms towards a more systematic and customised approach.”

That shift has resulted in the evolution of a deeper intelligence within algorithms that has built awareness of other orders in the marketplace, and executes the next trade accordingly. This approach has become more pertinent as low volumes increase the likelihood of information leakage by making larger orders more visible, and trading continues to fragment across venues, each of which has its own technology, concentration in particular stocks and client base. Bradley Duke, a managing director at broker Knight Capital Europe, said: “If the liquidity in the marketplace was fragmented before, today it’s completely fractured.”

Order awareness algorithms combat this by gathering feedback and data during the execution of one order, which helps to determine where the next order should be placed. This allows a trade to be targeted on a specific venue, increasing its speed and preventing its intentions being broadcast to other market participants.

Duke said: “A spray approach is not always efficient because of the signals it gives to the market. It is sometimes better to be more precise and go to fewer venues.”

A human touch?

However, in a time of difficult market conditions, a more flexible approach is also recommended.
Richard Balarkas, president and chief executive of broker Instinet Europe, said: “The conditions dictate that we need to take a much more attentive and intensive approach to the working of our algorithms. Traders are taking a far greater degree of manual control and switching between the systems we use and venues we access.”

Other participants argue this is the time to meet clients and tailor algorithms to their specific needs.
Brian Gallagher, managing director and head of European electronic equities at Morgan Stanley, said: “We’re encouraging teams to use the opportunity to go out and reconnect with clients. You can’t force the market to trade, nor would we want to, but we take this slowdown as an opportunity to sit down with clients and walk them through our algorithms and liquidity strategies.”

However, while innovation and investment is the answer to the low levels of activity for some industry practitioners, for others there is talk of getting back to basics, removing a reliance on algorithmic trading and hitting the phones instead.

Gallagher said: “A low-volume, low volatility market de-emphasises electronic execution to a certain extent – the business underperforms during dips in the market but outperforms when activity picks up.”

• Playing copy-cat

Demand for a new breed of algorithm that attempts to mimic the logic of high frequency trading firms is gathering momentum.

The development is in response to the increasing volume of trades being executed by high frequency trading firms, which trade in a fraction of a second, often attempting to profit from small price discrepancies across venues as well as providing liquidity to markets.

They have attracted controversy among traditional investors: 48% of buyside firms listed them as one of
the most important issues facing the buyside, according to a recent survey of European investors by US consultancy Tabb Group. Critics argue that these firms cause instability in the market and were widely blamed for contributing towards the flash crash on May 6 last year, when the Dow Jones Industrial average plummeted nearly 10% in 20 minutes.

Stuart Baden Powell, head of European electronic trading strategy at RBC Capital Markets, said: “Pre-crash, many buyside firms were simply demanding liquidity. Now, the more insightful buyside desks are demanding improved quality from the way algorithms interact with high frequency trading.”

US high frequency trading firm Getco this month made the unusual move of making its proprietary algorithms available to buyside firms. Getco said it would help investors “to mimic the order execution behaviours of a dedicated marketmaker”. The move came two months after Deutsche Bank revealed it had updated its Stealth suite of algorithms, using high frequency models to source liquidity. The Stealth model uses short-term price forecasts to determine which venues to access and allows customers to match how much they are willing to pay with how fast they want a transaction completed.